Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI

Abstract

A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological "atoms". Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal).

Figure 1.

Parcels (blue) from 333-area parcellations using global similarity, local gradient, and gwMRF approaches overlaid on histological (red) boundaries of areas 44 and 3. The local gradient approach overestimates the posterior boundary of area 44. On the other hand, the “bleeding” of cortical parcels across the central sulcus (area 3) could not be easily avoided without taking into account local gradients. Parcels from the gwMRF fusion approach agree well with both areas.

Figure 2.

Functional inhomogeneity measured by standard deviation of fMRI task activation within each parcel, and averaged across all parcels and contrasts of each cognitive domain (N = 745). Lower standard deviation indicates higher functional homogeneity. Our approach generated parcellations more functionally homogeneous than (A) Gordon (P ≈ 0), (B) Shen (P ≈ 0), (C) Craddock (P ≈ 0), and (D) Glasser (P = 4.8e−229). It is worth mentioning that the Glasser parcellation was partially derived from this data set.

Figure 3.

Connectional homogeneity as measured by rs-fMRI computed on the (A) GSP test set (N = 745) in fsaverage space, (B) HCP data set (N = 820) in FSLR space, and (C) NKI data set (N = 205) in MNI space. The gwMRF fusion approach generated parcellations that achieved better connectional homogeneity than all other parcellations across all 3 data sets (P < 2.8e−28 for all comparisons). There is no error bar for the HCP data because the HCP group-average dense connectivity matrix was utilized. It is worth mentioning that the Glasser parcellation was partially derived from the HCP data set.

Figure 4.

Network structure is preserved in the 400-area parcellation. First row shows 7 and 17 networks from Yeo et al. (2011). Second row shows each gwMRF parcel assigned a network color based on spatial overlap with networks from Yeo et al. (2011). Last row shows community structure of gwMRF parcellation after clustering. Observe striking similarity between second and third rows, suggesting that network structure of the original resolution data is preserved in the 400-area parcellation. Results for 600-area, 800-area, and 1000-area parcellations can be found in Supplementary Figures S6, S7, and S8.

Figure 5.

Parcels (blue) of the 400-area cerebral cortex parcellation overlaid on histological (red) boundaries of left hemisphere (A) area 3, (B) area 4, (C) area 2, (D) hOc5, and (E) area 17. Other histological areas are found in Supplementary Figure S9.

Figure 6.

Group-average task activation maps of (A) tongue motion (tongue—average motor), (B1) right finger tapping (right finger—average motor), (B2) left finger tapping (left finger—average motor), and (D) language task (story—math) from the HCP data set overlaid on boundaries (black) of 400-area cerebral cortex parcellation. Other task activation maps with different thresholds can be found in Supplementay Figures S11A–S11E.

Figure 7.

Functional connectivity homogeneity of each parcel of 400-area cerebral cortex parcellation based on (A) GSP and (B) HCP data sets. Overall homogeneity for the GSP and HCP data sets were 0.58 and 0.78, respectively. Note that the parcellation was derived from the GSP data set. However, acquisition and processing differences might lead to higher homogeneity in the HCP data set.